from math import sqrt
from numpy import concatenate
import numpy as np
import matplotlib.pyplot as pyplot
import matplotlib.pyplot as plt
from pandas import read_csv
from pandas import DataFrame
from pandas import concat
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import mean_squared_error
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
import sys


# convert series to supervised learning
def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):
    n_vars = 1 if type(data) is list else data.shape[1]
    df = DataFrame(data)
    cols, names = list(), list()
    # input sequence (t-n, ... t-1)
    for i in range(n_in, 0, -1):
        cols.append(df.shift(i))
        names += [('var%d(t-%d)' % (j + 1, i)) for j in range(n_vars)]
    # forecast sequence (t, t+1, ... t+n)
    for i in range(0, n_out):
        cols.append(df.shift(-i))
        if i == 0:
            names += [('var%d(t)' % (j + 1)) for j in range(n_vars)]
        else:
            names += [('var%d(t+%d)' % (j + 1, i)) for j in range(n_vars)]
    # put it all together
    agg = concat(cols, axis=1)
    agg.columns = names
    # drop rows with NaN values
    if dropnan:
        agg.dropna(inplace=True)
    return agg


# load dataset
train_df = pd.read_csv('data/train/train_FD001.txt', sep=" ", header=None, index_col=0)
train_df.drop(train_df.columns[[25, 26]], axis=1, inplace=True)
train_df.index.name = 'id'
train_df.columns = ['cycle', 'setting1', 'setting2', 'setting3', 's1', 's2', 's3',
                    's4', 's5', 's6', 's7', 's8', 's9', 's10', 's11', 's12', 's13', 's14',
                    's15', 's16', 's17', 's18', 's19', 's20', 's21']
train_df.drop(train_df.columns[[0, 1, 2, 3, 4, 8, 9, 13, 19, 21, 22]], axis=1, inplace=True)

values = train_df.values

# specify columns to plot
#groups = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
#i = 1
# plot each column
#pyplot.figure()
#for group in groups:
#    pyplot.subplot(len(groups), 1, i)
#    pyplot.plot(values[:, group])
#    pyplot.title(train_df.columns[group], y=0.5, loc='right')
#    i += 1
#pyplot.show()

# ensure all data is float
values = values.astype('float32')
# normalize features
scaler = MinMaxScaler(feature_range=(0, 1))
scaled = scaler.fit_transform(values)
# frame as supervised learning
reframed = series_to_supervised(scaled, 1, 1)
# drop columns we don't want to predict
reframed.drop(reframed.columns[[15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]], axis=1, inplace=True)
print(reframed.head().shape)
reframed.to_csv('reframed.csv', encoding='utf-8', index=None)

# split into train and test sets
values = reframed.values
n_train_cycles = 20430
train = values[:n_train_cycles, :]
test = values[n_train_cycles:, :]
# split into input and outputs
train_X, train_y = train[:, :-1], train[:, -1]
test_X, test_y = test[:, :-1], test[:, -1]
# reshape input to be 3D [samples, timesteps, features]
train_X = train_X.reshape((train_X.shape[0], 1, train_X.shape[1]))
test_X = test_X.reshape((test_X.shape[0], 1, test_X.shape[1]))
print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)

# design network
model = Sequential()
model.add(LSTM(15, input_shape=(train_X.shape[1], train_X.shape[2])))
model.add(Dense(1))
model.compile(loss='mae', optimizer='adam')
# fit network
history = model.fit(train_X, train_y, epochs=200, batch_size=500, validation_data=(test_X, test_y), verbose=2, shuffle=False)

# plot history
pyplot.plot(history.history['loss'], label='train')
pyplot.plot(history.history['val_loss'], label='test')
pyplot.legend()
pyplot.show()

# make a prediction
yhat = model.predict(test_X)
test_X = test_X.reshape((test_X.shape[0], test_X.shape[2]))

# invert scaling for forecast
inv_yhat = concatenate((yhat, test_X[:, 1:]), axis=1)
inv_yhat = scaler.inverse_transform(inv_yhat)
inv_yhat = inv_yhat[:, 0]
# invert scaling for actual
test_y = test_y.reshape((len(test_y), 1))
inv_y = concatenate((test_y, test_X[:, 1:]), axis=1)
inv_y = scaler.inverse_transform(inv_y)
inv_y = inv_y[:, 0]
# calculate RMSE
rmse = sqrt(mean_squared_error(inv_y, inv_yhat))
print('Test RMSE: %.3f' % rmse)

plt.plot(inv_yhat, label='predicted')
plt.plot(inv_y, label='real')
plt.legend()
plt.show()
